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相关概念视频

Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
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Reinforcement Schedules01:24

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Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
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Operant conditioning serves as a foundational principle in therapeutic interventions aimed at modifying maladaptive behaviors. Central to this approach is the notion that behaviors, both adaptive and maladaptive, are learned through reinforcement. By analyzing the environmental factors that reinforce problematic behaviors, clinicians can design interventions to weaken these reinforcements and replace maladaptive behaviors with healthier alternatives.
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A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning
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RIS辅助的主动移动网络下链干扰抑制:一种深度强化学习方法

Yingze Wang1, Mengying Sun1, Qimei Cui1

  • 1National Engineering Laboratory for Mobile Network Technologies, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Sensors (Basel, Switzerland)
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PubMed
概括
此摘要是机器生成的。

本研究介绍了可多重配置的智能表面 (RIS),以增强主动移动网络 (PMN). RIS技术显著抑制干扰,提高了低延迟通信系统的网络容量和可靠性.

关键词:
异步优势演员关键 (A3C)干扰抑制抑制干扰抑制积极的移动网络 (PMN)可重新配置的智能表面 (RIS)强化学习 (RL) 是一种强化学习.

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科学领域:

  • 无线通信网络是无线通信网络.
  • 信号处理 信号处理
  • 人工智能的人工智能是人工智能.

背景情况:

  • 积极的移动网络 (PMN) 提供低延迟通信,但由于开放循环传输和虚拟电池技术而遭受干扰和可靠性问题.
  • 在具有有限通道状态信息的复杂,时间变化的PMN环境中管理可重新配置的智能表面 (RIS) 是一个挑战.

研究的目的:

  • 加强主动移动网络 (PMN) 的干扰抑制能力和整体能力.
  • 解决在动态PMN环境中管理多重配置智能表面 (RIS) 的挑战.

主要方法:

  • 制定了RIS相位移和反射系数的优化问题.
  • 开发了一个深度强化学习 (DRL) 方法,使用异步优势演员-关键 (A3C) 算法.
  • 为PMN-RIS系统量身定制的特定行动空间,状态空间和奖励功能.

主要成果:

  • 在PMN区域部署RIS显著抑制了用户干扰.
  • 拟议的基于A3C的RIS管理方案在网络容量方面表现优于基准方法.
  • 随着部署的RIS数量的增加,能力改进接近理论限制.

结论:

  • 多重重配置智能表面 (RIS) 有效地减轻主动移动网络 (PMN) 中的干扰.
  • 深度强化学习,特别是A3C算法,为PMN中的动态RIS管理提供了有效的解决方案.
  • 整合RIS技术对改善未来低延迟通信系统的性能和容量具有重大前景.